Low-Cost Data-Driven Surrogate Modeling of Antenna Structures by Constrained Sampling

Full-wave electromagnetic analysis has become one of the major design tools for contemporary antenna structures. Although reliable, it is computationally expensive, which makes automated simulation-driven antenna design (e.g., parametric optimization) difficult. This difficulty can be alleviated by utilization of fast and accurate replacement models (surrogates). Unfortunately, conventional data-driven modeling of antennas is usually prohibitively expensive in terms of data acquisition or even infeasible (due to numerical problems with handling large training datasets). In this letter, a technique for reduced-cost antenna modeling is discussed. It exploits constrained sampling within a restricted region of the design space, defined by the reference designs optimized for selected sets of performance requirements. The proposed approach permits dramatic reduction of the sampled portion of the design space without formally reducing geometry parameter ranges. It is demonstrated using two antenna structures and favorably compared to conventional modeling using kriging interpolation.